{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6fe1f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch            : 1.10.1\n",
      "pytorch_lightning: 1.5.1\n",
      "torchmetrics     : 0.6.2\n",
      "matplotlib       : 3.3.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2b50ba",
   "metadata": {},
   "source": [
    "The three extensions below are optional, for more information, see\n",
    "- `watermark`:  https://github.com/rasbt/watermark\n",
    "- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\n",
    "- `nb_black`: https://github.com/dnanhkhoa/nb_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bbd8b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext pycodestyle_magic\n",
    "%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a921ddea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_formatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext nb_black"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7975ec2",
   "metadata": {},
   "source": [
    "<a href=\"https://pytorch.org\"><img src=\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\" width=\"90\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\"https://www.pytorchlightning.ai\"><img src=\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\" width=\"150\"/></a>\n",
    "\n",
    "# AlexNet with Grouped Convolutions Trained on CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d929930",
   "metadata": {},
   "source": [
    "AlexNet [1][2] trained on CIFAR-10 [3].\n",
    "\n",
    "This implementation uses grouped convolutions like in the original AlexNet paper [2]:\n",
    "\n",
    "![](../../pytorch_ipynb/images/alexnet/alexnet-paper.png)\n",
    "\n",
    "Here, the network is essentially split into two parts to train it on two GPUs with 1.5 Gb RAM each. This was purely done for computational performance reasons (and the video RAM limitation back then). However, there are certain benefits to using grouped convolutions ...\n",
    "\n",
    "\n",
    "**Taking a step back, how do grouped convolutions work?**\n",
    "\n",
    "In a nutshell, you can think of grouped convolutions as convolutional layers that process part of the input independently and merge the results. So, for example, if you consider grouped convolutions with two filter groups, each filter group would process half of the channels. \n",
    "\n",
    "![](../../pytorch_ipynb/images/alexnet/grouped-convolutions.png)\n",
    "\n",
    "**One of the benefits of grouped convolutions is**, as noted by Yani Ioannou [4], that AlexNet has a slightly improved accuracy when using two filter groups:\n",
    "\n",
    "![](../../pytorch_ipynb/images/alexnet/alexnet-groups.png)\n",
    "\n",
    "**Another benefit is the reduced parameter size**. \n",
    "\n",
    "Say we have kernels with height 3 and width 3. The inputs have 6 channels, and the output channels are set to 12. Then, we have kernels with 3x3x6 weight parameters with a regular convolution. Since we have 12 output channels, that's 3x3x6x12=648 parameters in total.\n",
    "\n",
    "Now, let's assume we use a grouped convolution with group size 2. We still have a 3x3 kernel height and width. But now, the number of input channels is split by a factor of 2, so each kernel is 3x3x3. The first group produces the first 6 output channels, so we have 3x3x3x6 parameters for the first group. The second group has the same size, so we have (3x3x3x6)x2 = 3x3x3x12 = 324, which is a 2x reduction in parameters compared to the regular convolution.\n",
    "\n",
    "\n",
    "**And how do we do this in PyTorch?**\n",
    "\n",
    "Implementing grouped convolutions in PyTorch is now really straightforward. We just used the `groups` parameter. For example, to implement a grouped convolution with two filter groups we use\n",
    "\n",
    "    torch.nn.Conv2d(..., groups=2)\n",
    "\n",
    "Note that a requirement for this is that the number of input and output channels is divisible by groups (here: 2).\n",
    "\n",
    "### References\n",
    "\n",
    "- [1] L13.7 CNN Architectures & AlexNet (20:17), https://www.youtube.com/watch?v=-IHxe4-09e4\n",
    "- [2] Imagenet classification with deep convolutional neural networks, https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\n",
    "- [3] https://en.wikipedia.org/wiki/CIFAR-10\n",
    "- [4] https://blog.yani.ai/filter-group-tutorial/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c61f88",
   "metadata": {},
   "source": [
    "## General settings and hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0588c1b3",
   "metadata": {},
   "source": [
    "- Here, we specify some general hyperparameter values and general settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "83ce1a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"BATCH_SIZE = 256\\nNUM_EPOCHS = 40\\nLEARNING_RATE = 0.0001\\nNUM_WORKERS = 4\";\n",
       "                var nbb_formatted_code = \"BATCH_SIZE = 256\\nNUM_EPOCHS = 40\\nLEARNING_RATE = 0.0001\\nNUM_WORKERS = 4\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
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   ],
   "source": [
    "BATCH_SIZE = 256\n",
    "NUM_EPOCHS = 40\n",
    "LEARNING_RATE = 0.0001\n",
    "NUM_WORKERS = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883ece2d",
   "metadata": {},
   "source": [
    "- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c56da1dd",
   "metadata": {},
   "source": [
    "## Implementing a Neural Network using PyTorch Lightning's `LightningModule`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d65d8d2",
   "metadata": {},
   "source": [
    "- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\n",
    "- In essence, `LightningModule` is a wrapper around a PyTorch module.\n",
    "- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7641c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"import torch.nn as nn\\nimport torch.nn.functional as F\\n\\n\\n# Regular PyTorch Module\\nclass PyTorchModel(nn.Module):\\n    def __init__(self, num_classes):\\n        super().__init__()\\n        self.features = nn.Sequential(\\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n            \\n            nn.Conv2d(64, 192, kernel_size=5, padding=2, groups=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n            \\n            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),\\n            nn.ReLU(inplace=True),\\n            \\n            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),\\n            nn.ReLU(inplace=True),\\n            \\n            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n        )\\n        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\n        self.classifier = nn.Sequential(\\n            nn.Dropout(0.5),\\n            nn.Linear(256 * 6 * 6, 4096),\\n            nn.ReLU(inplace=True),\\n            nn.Dropout(0.5),\\n            nn.Linear(4096, 4096),\\n            nn.ReLU(inplace=True),\\n            nn.Linear(4096, num_classes)\\n        )\\n\\n    def forward(self, x):\\n        x = self.features(x)\\n        x = self.avgpool(x)\\n        x = torch.flatten(x, start_dim=1)\\n        logits = self.classifier(x)\\n        return logits\";\n",
       "                var nbb_formatted_code = \"import torch.nn as nn\\nimport torch.nn.functional as F\\n\\n\\n# Regular PyTorch Module\\nclass PyTorchModel(nn.Module):\\n    def __init__(self, num_classes):\\n        super().__init__()\\n        self.features = nn.Sequential(\\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n            nn.Conv2d(64, 192, kernel_size=5, padding=2, groups=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),\\n            nn.ReLU(inplace=True),\\n            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),\\n            nn.ReLU(inplace=True),\\n            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n        )\\n        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\n        self.classifier = nn.Sequential(\\n            nn.Dropout(0.5),\\n            nn.Linear(256 * 6 * 6, 4096),\\n            nn.ReLU(inplace=True),\\n            nn.Dropout(0.5),\\n            nn.Linear(4096, 4096),\\n            nn.ReLU(inplace=True),\\n            nn.Linear(4096, num_classes),\\n        )\\n\\n    def forward(self, x):\\n        x = self.features(x)\\n        x = self.avgpool(x)\\n        x = torch.flatten(x, start_dim=1)\\n        logits = self.classifier(x)\\n        return logits\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
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       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
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   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "# Regular PyTorch Module\n",
    "class PyTorchModel(nn.Module):\n",
    "    def __init__(self, num_classes):\n",
    "        super().__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            \n",
    "            nn.Conv2d(64, 192, kernel_size=5, padding=2, groups=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            \n",
    "            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            \n",
    "            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            \n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "        )\n",
    "        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(256 * 6 * 6, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(4096, num_classes)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.features(x)\n",
    "        x = self.avgpool(x)\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        logits = self.classifier(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b410e766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\nimport pytorch_lightning as pl\\nimport torchmetrics\\n\\n\\n# LightningModule that receives a PyTorch model as input\\nclass LightningModel(pl.LightningModule):\\n    def __init__(self, model, learning_rate):\\n        super().__init__()\\n\\n        self.learning_rate = learning_rate\\n        # The inherited PyTorch module\\n        self.model = model\\n        if hasattr(model, \\\"dropout_proba\\\"):\\n            self.dropout_proba = model.dropout_proba\\n\\n        # Save settings and hyperparameters to the log directory\\n        # but skip the model parameters\\n        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\\n        # Set up attributes for computing the accuracy\\n        self.train_acc = torchmetrics.Accuracy()\\n        self.valid_acc = torchmetrics.Accuracy()\\n        self.test_acc = torchmetrics.Accuracy()\\n\\n    # Defining the forward method is only necessary\\n    # if you want to use a Trainer's .predict() method (optional)\\n    def forward(self, x):\\n        return self.model(x)\\n\\n    # A common forward step to compute the loss and labels\\n    # this is used for training, validation, and testing below\\n    def _shared_step(self, batch):\\n        features, true_labels = batch\\n        logits = self(features)\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n        predicted_labels = torch.argmax(logits, dim=1)\\n\\n        return loss, true_labels, predicted_labels\\n\\n    def training_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"train_loss\\\", loss)\\n\\n        # Do another forward pass in .eval() mode to compute accuracy\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\n        # during evaluation (inference)\\n        self.model.eval()\\n        with torch.no_grad():\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\n        self.train_acc(predicted_labels, true_labels)\\n        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n        self.model.train()\\n\\n        return loss  # this is passed to the optimzer for training\\n\\n    def validation_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"valid_loss\\\", loss)\\n        self.valid_acc(predicted_labels, true_labels)\\n        self.log(\\n            \\\"valid_acc\\\",\\n            self.valid_acc,\\n            on_epoch=True,\\n            on_step=False,\\n            prog_bar=True,\\n        )\\n\\n    def test_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.test_acc(predicted_labels, true_labels)\\n        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\\n    def configure_optimizers(self):\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n        return optimizer\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\nimport pytorch_lightning as pl\\nimport torchmetrics\\n\\n\\n# LightningModule that receives a PyTorch model as input\\nclass LightningModel(pl.LightningModule):\\n    def __init__(self, model, learning_rate):\\n        super().__init__()\\n\\n        self.learning_rate = learning_rate\\n        # The inherited PyTorch module\\n        self.model = model\\n        if hasattr(model, \\\"dropout_proba\\\"):\\n            self.dropout_proba = model.dropout_proba\\n\\n        # Save settings and hyperparameters to the log directory\\n        # but skip the model parameters\\n        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\\n        # Set up attributes for computing the accuracy\\n        self.train_acc = torchmetrics.Accuracy()\\n        self.valid_acc = torchmetrics.Accuracy()\\n        self.test_acc = torchmetrics.Accuracy()\\n\\n    # Defining the forward method is only necessary\\n    # if you want to use a Trainer's .predict() method (optional)\\n    def forward(self, x):\\n        return self.model(x)\\n\\n    # A common forward step to compute the loss and labels\\n    # this is used for training, validation, and testing below\\n    def _shared_step(self, batch):\\n        features, true_labels = batch\\n        logits = self(features)\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n        predicted_labels = torch.argmax(logits, dim=1)\\n\\n        return loss, true_labels, predicted_labels\\n\\n    def training_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"train_loss\\\", loss)\\n\\n        # Do another forward pass in .eval() mode to compute accuracy\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\n        # during evaluation (inference)\\n        self.model.eval()\\n        with torch.no_grad():\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\n        self.train_acc(predicted_labels, true_labels)\\n        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n        self.model.train()\\n\\n        return loss  # this is passed to the optimzer for training\\n\\n    def validation_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"valid_loss\\\", loss)\\n        self.valid_acc(predicted_labels, true_labels)\\n        self.log(\\n            \\\"valid_acc\\\",\\n            self.valid_acc,\\n            on_epoch=True,\\n            on_step=False,\\n            prog_bar=True,\\n        )\\n\\n    def test_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.test_acc(predicted_labels, true_labels)\\n        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\\n    def configure_optimizers(self):\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n        return optimizer\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   ],
   "source": [
    "# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\n",
    "import pytorch_lightning as pl\n",
    "import torchmetrics\n",
    "\n",
    "\n",
    "# LightningModule that receives a PyTorch model as input\n",
    "class LightningModel(pl.LightningModule):\n",
    "    def __init__(self, model, learning_rate):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        # The inherited PyTorch module\n",
    "        self.model = model\n",
    "        if hasattr(model, \"dropout_proba\"):\n",
    "            self.dropout_proba = model.dropout_proba\n",
    "\n",
    "        # Save settings and hyperparameters to the log directory\n",
    "        # but skip the model parameters\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        # Set up attributes for computing the accuracy\n",
    "        self.train_acc = torchmetrics.Accuracy()\n",
    "        self.valid_acc = torchmetrics.Accuracy()\n",
    "        self.test_acc = torchmetrics.Accuracy()\n",
    "\n",
    "    # Defining the forward method is only necessary\n",
    "    # if you want to use a Trainer's .predict() method (optional)\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    # A common forward step to compute the loss and labels\n",
    "    # this is used for training, validation, and testing below\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "        loss = torch.nn.functional.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"train_loss\", loss)\n",
    "\n",
    "        # Do another forward pass in .eval() mode to compute accuracy\n",
    "        # while accountingfor Dropout, BatchNorm etc. behavior\n",
    "        # during evaluation (inference)\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            _, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.train_acc(predicted_labels, true_labels)\n",
    "        self.log(\"train_acc\", self.train_acc, on_epoch=True, on_step=False)\n",
    "        self.model.train()\n",
    "\n",
    "        return loss  # this is passed to the optimzer for training\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"valid_loss\", loss)\n",
    "        self.valid_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"valid_acc\",\n",
    "            self.valid_acc,\n",
    "            on_epoch=True,\n",
    "            on_step=False,\n",
    "            prog_bar=True,\n",
    "        )\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc, on_epoch=True, on_step=False)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06de7ac",
   "metadata": {},
   "source": [
    "## Setting up the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9cffd20",
   "metadata": {},
   "source": [
    "- In this section, we are going to set up our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dac1f73",
   "metadata": {},
   "source": [
    "### Inspecting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be3c17e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "\n",
      "Training label distribution:\n",
      "\n",
      "Test label distribution:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0, 1000),\n",
       " (1, 1000),\n",
       " (2, 1000),\n",
       " (3, 1000),\n",
       " (4, 1000),\n",
       " (5, 1000),\n",
       " (6, 1000),\n",
       " (7, 1000),\n",
       " (8, 1000),\n",
       " (9, 1000)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/dataset_cifar10_check.py\\nfrom collections import Counter\\nfrom torchvision import datasets\\nfrom torchvision import transforms\\nfrom torch.utils.data import DataLoader\\n\\n\\ntrain_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n)\\n\\ntrain_loader = DataLoader(\\n    dataset=train_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=True,\\n    shuffle=True,\\n)\\n\\ntest_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n)\\n\\ntest_loader = DataLoader(\\n    dataset=test_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=False,\\n    shuffle=False,\\n)\\n\\ntrain_counter = Counter()\\nfor images, labels in train_loader:\\n    train_counter.update(labels.tolist())\\n\\ntest_counter = Counter()\\nfor images, labels in test_loader:\\n    test_counter.update(labels.tolist())\\n\\nprint(\\\"\\\\nTraining label distribution:\\\")\\nsorted(train_counter.items())\\n\\nprint(\\\"\\\\nTest label distribution:\\\")\\nsorted(test_counter.items())\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/dataset_cifar10_check.py\\nfrom collections import Counter\\nfrom torchvision import datasets\\nfrom torchvision import transforms\\nfrom torch.utils.data import DataLoader\\n\\n\\ntrain_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n)\\n\\ntrain_loader = DataLoader(\\n    dataset=train_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=True,\\n    shuffle=True,\\n)\\n\\ntest_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n)\\n\\ntest_loader = DataLoader(\\n    dataset=test_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=False,\\n    shuffle=False,\\n)\\n\\ntrain_counter = Counter()\\nfor images, labels in train_loader:\\n    train_counter.update(labels.tolist())\\n\\ntest_counter = Counter()\\nfor images, labels in test_loader:\\n    test_counter.update(labels.tolist())\\n\\nprint(\\\"\\\\nTraining label distribution:\\\")\\nsorted(train_counter.items())\\n\\nprint(\\\"\\\\nTest label distribution:\\\")\\nsorted(test_counter.items())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/dataset_cifar10_check.py\n",
    "from collections import Counter\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "train_dataset = datasets.CIFAR10(\n",
    "    root=\"./data\", train=True, transform=transforms.ToTensor(), download=True\n",
    ")\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_workers=NUM_WORKERS,\n",
    "    drop_last=True,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "test_dataset = datasets.CIFAR10(\n",
    "    root=\"./data\", train=False, transform=transforms.ToTensor()\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_workers=NUM_WORKERS,\n",
    "    drop_last=False,\n",
    "    shuffle=False,\n",
    ")\n",
    "\n",
    "train_counter = Counter()\n",
    "for images, labels in train_loader:\n",
    "    train_counter.update(labels.tolist())\n",
    "\n",
    "test_counter = Counter()\n",
    "for images, labels in test_loader:\n",
    "    test_counter.update(labels.tolist())\n",
    "\n",
    "print(\"\\nTraining label distribution:\")\n",
    "sorted(train_counter.items())\n",
    "\n",
    "print(\"\\nTest label distribution:\")\n",
    "sorted(test_counter.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e239588",
   "metadata": {},
   "source": [
    "### Performance baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0f6de7c",
   "metadata": {},
   "source": [
    "- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\n",
    "- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "041c2950",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Majority class: 3\n",
      "Accuracy when always predicting the majority class:\n",
      "0.10 (10.00%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/performance_baseline.py\\nmajority_class = test_counter.most_common(1)[0]\\nprint(\\\"Majority class:\\\", majority_class[0])\\n\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\nprint(\\\"Accuracy when always predicting the majority class:\\\")\\nprint(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/performance_baseline.py\\nmajority_class = test_counter.most_common(1)[0]\\nprint(\\\"Majority class:\\\", majority_class[0])\\n\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\nprint(\\\"Accuracy when always predicting the majority class:\\\")\\nprint(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/performance_baseline.py\n",
    "majority_class = test_counter.most_common(1)[0]\n",
    "print(\"Majority class:\", majority_class[0])\n",
    "\n",
    "baseline_acc = majority_class[1] / sum(test_counter.values())\n",
    "print(\"Accuracy when always predicting the majority class:\")\n",
    "print(f\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b95e4c1",
   "metadata": {},
   "source": [
    "## A quick visual check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "546e593f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/plot_visual-check_basic.py\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport torchvision\\n\\n\\nfor images, labels in train_loader:  \\n    break\\n\\nplt.figure(figsize=(8, 8))\\nplt.axis(\\\"off\\\")\\nplt.title(\\\"Training images\\\")\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\n    images[:64], \\n    padding=2,\\n    normalize=True),\\n    (1, 2, 0)))\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/plot_visual-check_basic.py\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport torchvision\\n\\n\\nfor images, labels in train_loader:\\n    break\\n\\nplt.figure(figsize=(8, 8))\\nplt.axis(\\\"off\\\")\\nplt.title(\\\"Training images\\\")\\nplt.imshow(\\n    np.transpose(\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\n    )\\n)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/plot_visual-check_basic.py\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torchvision\n",
    "\n",
    "\n",
    "for images, labels in train_loader:  \n",
    "    break\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Training images\")\n",
    "plt.imshow(np.transpose(torchvision.utils.make_grid(\n",
    "    images[:64], \n",
    "    padding=2,\n",
    "    normalize=True),\n",
    "    (1, 2, 0)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f4cf99",
   "metadata": {},
   "source": [
    "### Setting up a `DataModule`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af8ed10b",
   "metadata": {},
   "source": [
    "- There are three main ways we can prepare the dataset for Lightning. We can\n",
    "  1. make the dataset part of the model;\n",
    "  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\n",
    "  3. create a LightningDataModule.\n",
    "- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9abaa47d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"import os\\n\\nfrom torch.utils.data.dataset import random_split\\nfrom torch.utils.data import DataLoader\\nfrom torchvision import transforms\\n\\n\\nclass DataModule(pl.LightningDataModule):\\n    def __init__(self, data_path=\\\"./\\\"):\\n        super().__init__()\\n        self.data_path = data_path\\n\\n    def prepare_data(self):\\n        datasets.CIFAR10(root=self.data_path, download=True)\\n\\n        self.train_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.RandomCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n\\n        self.test_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.CenterCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n        return\\n\\n    def setup(self, stage=None):\\n        train = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=True,\\n            transform=self.train_transform,\\n            download=False,\\n        )\\n\\n        self.test = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=False,\\n            transform=self.test_transform,\\n            download=False,\\n        )\\n\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\\n    def train_dataloader(self):\\n        train_loader = DataLoader(\\n            dataset=self.train,\\n            batch_size=BATCH_SIZE,\\n            drop_last=True,\\n            shuffle=True,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return train_loader\\n\\n    def val_dataloader(self):\\n        valid_loader = DataLoader(\\n            dataset=self.valid,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return valid_loader\\n\\n    def test_dataloader(self):\\n        test_loader = DataLoader(\\n            dataset=self.test,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return test_loader\";\n",
       "                var nbb_formatted_code = \"import os\\n\\nfrom torch.utils.data.dataset import random_split\\nfrom torch.utils.data import DataLoader\\nfrom torchvision import transforms\\n\\n\\nclass DataModule(pl.LightningDataModule):\\n    def __init__(self, data_path=\\\"./\\\"):\\n        super().__init__()\\n        self.data_path = data_path\\n\\n    def prepare_data(self):\\n        datasets.CIFAR10(root=self.data_path, download=True)\\n\\n        self.train_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.RandomCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n\\n        self.test_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.CenterCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n        return\\n\\n    def setup(self, stage=None):\\n        train = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=True,\\n            transform=self.train_transform,\\n            download=False,\\n        )\\n\\n        self.test = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=False,\\n            transform=self.test_transform,\\n            download=False,\\n        )\\n\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\\n    def train_dataloader(self):\\n        train_loader = DataLoader(\\n            dataset=self.train,\\n            batch_size=BATCH_SIZE,\\n            drop_last=True,\\n            shuffle=True,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return train_loader\\n\\n    def val_dataloader(self):\\n        valid_loader = DataLoader(\\n            dataset=self.valid,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return valid_loader\\n\\n    def test_dataloader(self):\\n        test_loader = DataLoader(\\n            dataset=self.test,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return test_loader\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "import os\n",
    "\n",
    "from torch.utils.data.dataset import random_split\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "\n",
    "class DataModule(pl.LightningDataModule):\n",
    "    def __init__(self, data_path=\"./\"):\n",
    "        super().__init__()\n",
    "        self.data_path = data_path\n",
    "\n",
    "    def prepare_data(self):\n",
    "        datasets.CIFAR10(root=self.data_path, download=True)\n",
    "\n",
    "        self.train_transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((70, 70)),\n",
    "                transforms.RandomCrop((64, 64)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        self.test_transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((70, 70)),\n",
    "                transforms.CenterCrop((64, 64)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "        return\n",
    "\n",
    "    def setup(self, stage=None):\n",
    "        train = datasets.CIFAR10(\n",
    "            root=self.data_path,\n",
    "            train=True,\n",
    "            transform=self.train_transform,\n",
    "            download=False,\n",
    "        )\n",
    "\n",
    "        self.test = datasets.CIFAR10(\n",
    "            root=self.data_path,\n",
    "            train=False,\n",
    "            transform=self.test_transform,\n",
    "            download=False,\n",
    "        )\n",
    "\n",
    "        self.train, self.valid = random_split(train, lengths=[45000, 5000])\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        train_loader = DataLoader(\n",
    "            dataset=self.train,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=True,\n",
    "            shuffle=True,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return train_loader\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        valid_loader = DataLoader(\n",
    "            dataset=self.valid,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=False,\n",
    "            shuffle=False,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return valid_loader\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        test_loader = DataLoader(\n",
    "            dataset=self.test,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=False,\n",
    "            shuffle=False,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return test_loader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d957b00d",
   "metadata": {},
   "source": [
    "- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \n",
    "- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf180925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"import torch\\n\\n\\ntorch.manual_seed(1) \\ndata_module = DataModule(data_path='./data')\";\n",
       "                var nbb_formatted_code = \"import torch\\n\\n\\ntorch.manual_seed(1)\\ndata_module = DataModule(data_path=\\\"./data\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "torch.manual_seed(1) \n",
    "data_module = DataModule(data_path='./data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8592d0e2",
   "metadata": {},
   "source": [
    "## Training the model using the PyTorch Lightning Trainer class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c905e372",
   "metadata": {},
   "source": [
    "- Next, we initialize our model.\n",
    "- Also, we define a call back to obtain the model with the best validation set performance after training.\n",
    "- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7660c860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"pytorch_model = PyTorchModel(num_classes=10)\";\n",
       "                var nbb_formatted_code = \"pytorch_model = PyTorchModel(num_classes=10)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "pytorch_model = PyTorchModel(num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01797000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\nfrom pytorch_lightning.loggers import CSVLogger\\n\\n\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\\ncallbacks = [\\n    ModelCheckpoint(\\n        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n    )  # save top 1 model\\n]\\nlogger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\nfrom pytorch_lightning.loggers import CSVLogger\\n\\n\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\\ncallbacks = [\\n    ModelCheckpoint(save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\")  # save top 1 model\\n]\\nlogger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/logger_csv_acc_basic.py\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "from pytorch_lightning.loggers import CSVLogger\n",
    "\n",
    "\n",
    "lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        save_top_k=1, mode=\"max\", monitor=\"valid_acc\"\n",
    "    )  # save top 1 model\n",
    "]\n",
    "logger = CSVLogger(save_dir=\"logs/\", name=\"my-model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e0ac279",
   "metadata": {},
   "source": [
    "- Now it's time to train our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ba6ef60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n",
      "  rank_zero_deprecation(\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type         | Params\n",
      "-------------------------------------------\n",
      "0 | model     | PyTorchModel | 55.8 M\n",
      "1 | train_acc | Accuracy     | 0     \n",
      "2 | valid_acc | Accuracy     | 0     \n",
      "3 | test_acc  | Accuracy     | 0     \n",
      "-------------------------------------------\n",
      "55.8 M    Trainable params\n",
      "0         Non-trainable params\n",
      "55.8 M    Total params\n",
      "223.289   Total estimated model params size (MB)\n"
     ]
    },
    {
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       "model_id": "3b34070fe52847ed8492ec8f9bbc440c",
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      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
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      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training took 10.80 min in total.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/trainer_nb_basic.py\\nimport time\\n\\n\\ntrainer = pl.Trainer(\\n    max_epochs=NUM_EPOCHS,\\n    callbacks=callbacks,\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\n    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n    logger=logger,\\n    deterministic=False,\\n    log_every_n_steps=10,\\n)\\n\\nstart_time = time.time()\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\n\\nruntime = (time.time() - start_time) / 60\\nprint(f\\\"Training took {runtime:.2f} min in total.\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/trainer_nb_basic.py\\nimport time\\n\\n\\ntrainer = pl.Trainer(\\n    max_epochs=NUM_EPOCHS,\\n    callbacks=callbacks,\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\n    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n    logger=logger,\\n    deterministic=False,\\n    log_every_n_steps=10,\\n)\\n\\nstart_time = time.time()\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\n\\nruntime = (time.time() - start_time) / 60\\nprint(f\\\"Training took {runtime:.2f} min in total.\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# %load ../code_lightningmodule/trainer_nb_basic.py\n",
    "import time\n",
    "\n",
    "\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=NUM_EPOCHS,\n",
    "    callbacks=callbacks,\n",
    "    progress_bar_refresh_rate=50,  # recommended for notebooks\n",
    "    accelerator=\"auto\",  # Uses GPUs or TPUs if available\n",
    "    devices=\"auto\",  # Uses all available GPUs/TPUs if applicable\n",
    "    logger=logger,\n",
    "    deterministic=False,\n",
    "    log_every_n_steps=10,\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "trainer.fit(model=lightning_model, datamodule=data_module)\n",
    "\n",
    "runtime = (time.time() - start_time) / 60\n",
    "print(f\"Training took {runtime:.2f} min in total.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601b1ca9",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43d6e92",
   "metadata": {},
   "source": [
    "- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9776f752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+tdzWphb5BYYJP+8o1xhKKeVpbisExpiFQEnDf90JfGWM2eVc/6C7shSr5V3gFwRLxxEXVYkbmsfwyeKdZOTklnsUpZTyFHFnb38iEgfMMsY0K2LZvwEHkACEAv8xxnxUzOuMAEYAREdHJ13OKECFZWVlERISct68hhvfIfrAjyzq+CFbs4MY9WsOg+Md9K/v79J7uKqobN5Cs7nOm/NpNtdU1Gw9evRYYYxpU+RCY4zbJiAOSC1m2TvAYqASEAVsBhpe6jWTkpKMq5KTky+emb7amFFhxvzyX2OMMfeMX2KSXvzOZJ/Jc/l9yiybl9BsrvPmfJrNNRU1G7DcFPO56slWQ3uAb40xJ40xh4GFQItyTxGTCLU7wrJxUFDAQ93rczjrDF+s2FPuUZRSyhM8WQimA11ExE9EgoH2wHqPJGn3ABzbAVu+p33dKrSqHcH7C7eSl1/gkThKKVWe3Nl8dDKwCGgkIntEZLiIjBSRkQDGmPXAt8AaYCnwgTGm2KambtXkRgipDkvfQ0R4qHsDdh/NZvbafR6Jo5RS5cltA9MYY+4oxTqvAa+5K0Op2R3Q5j5Y8A84spVejesRXy2EMQu2cmOL2KtyHFWllDrLd+8svlDSMLD5wfIJ2GzCyG712bA/k+SN5d+qVSmlypMWgrNCq0OTAbDqYzhzihtbxlIjIoh3k7d6OplSSrmVFoLC2t4POcch9UscdhsPdKnL8p3HWLajpPvilFKqYtNCUFidTtZQlsvGgTHc1rY2VSr5M2aBHhUopa5eWggKE4G2w2Hfati7giB/O/ddE8cPGw6yfp/2TKqUujppIbhQi9vBPxSWfQDA0I5xVPK3M/ZHPSpQSl2dtBBcKCDUKgapX8HJI4QHO7irQx1mrk5n15FTnk6nlFJlTgtBUdoOh/zTsGoSAMM718XPZmOMHhUopa5CWgiKUq0JxHWB5eOhIJ/osEDubF+bKct2sWTbEU+nU0qpMqWFoDhth8PxXbB5HgBP9W5ErcrBPDl1NVmn8zwcTimlyo4WguI07g+hMecuGlcK8OONIS3Ycyybl2d7pm88pZRyBy0ExbE7rG4ntnwPR7cB0DauCiO61GPy0l0kb9CuJ5RSVwctBCVpfa81tvHyCedmPX5dQxpFh/L0l2s4fuqMB8MppVTZ0EJQkrAY6xTRqo8hNxuAQIedN4a04OjJM/x1epqHAyql1JXTQnApbe+H7GPWfQVOzWqE82iveGauTmfWmnQPhlNKqSvnzoFpJojIQREpcbAZEWkrIvkiMthdWa5IXOff+h8q5MHu9WlRK4LnpqVyMCPHQ+GUUurKufOIYCLQp6QVRMQO/BOY68YcV+Zs/0Ppq2DPinOz/ew23hzSguwz+Tz95RqssaGVUqricVshMMYsBC7Vf/MfgC8B726Ck3gb+IfAT29AQf652fWrhvDMDY1J3niIz5bt9mBApZRynbjzm6yIxAGzjDHNilhWA/gU6AmMd643tZjXGQGMAIiOjk6aMmWKS3mysrIICQlxadvaO6dSb/skDkV1YH2TJyiwBwBQYAyvLcth+4kCXuwURNVg12rrlWRzN83mOm/Op9lcU1Gz9ejRY4Uxpk2RC40xbpuAOCC1mGVfAB2cjycCg0vzmklJScZVycnJLm9rjDFm0RhjRoUbM+5aY7IOn5u959gp0+xv35ob3/nZ5OTmeSabG2k213lzPs3mmoqaDVhuivlc9WSroTbAFBHZAQwG3hWRmzyY59I6jIQhH8H+NTD+Oji6HYAaEUG8dmsLVu8+zt9naJNSpVTF4rFCYIypa4yJM8bEAVOBh4wx0zyVp9Sa3ghDp0P2UasY7LUuIPdpVp2He9Rn8tLdTF66y8MhlVKq9NzZfHQysAhoJCJ7RGS4iIwUkZHues9yU7sDDJ8HjiCY2B82WY2enriuEV0bVmXU9DRW7Trm4ZBKKVU67mw1dIcxJsYY4zDG1DTGjDfGjDXGjC1i3WGmmAvFXisqHoZ/D1ENYfLtsPxD7Dbh7dtbEh0ewIMfr+RQ5mlPp1RKqUvSO4uvRGg0DJsN9XvCrMfgu+eICBDG3p3E8ewzPPzpSnLzCzydUimlSqSF4EoFhMAdU6DNcPj1v/C/ASSEnOTVWxJZuv0or8zRLquVUt5NC0FZsDug/5twywewbw2M7cxNoRu4r1McH/6yg2mr9no6oVJKFUsLQVlKvBVGLIBK1eDjQTwXPI32ceE889Ua0tJPeDqdUkoVSQtBWavaEB74AVrehf2nfzHJ/1XqBp5k5McrdPwCpZRX0kLgDv7BcNNoGDga//TlzPB7htoZK3lqqnZOp5TyPloI3KnV3fDAfBzBEUzyexn7hpl8vHinp1MppdR5tBC4W3QCjEhGarTmjcAP+GD2T2zYn+HpVEopdY4WgvIQEIoMGkeQ3fCm3xge/WQ52WfyL72dUkqVAy0E5aVKPWz9XieJNHoe/YwXZq3zdCKllAK0EJSvFndAws086f8FacuSmbN2n6cTKaWUFoJyJQL938IWWp0xwWN4/sul7Dl2ytOplFI+TgtBeQuqjNzyPrH5+/iTmchjU1LI0/6IlFIepIXAE+I6I50fZ5D8QNTuubz9wxZPJ1JK+TAtBJ7S/VmIbcWbQeOZ+sNiNhzVVkRKKc9w58A0E0TkoIikFrP8LhFZ45x+FZEW7srilfz8YdB4gmz5vBM8jvdXZ5Oy+7inUymlfJA7jwgmAn1KWL4d6GaMSQReBN53YxbvFFkfueGftM5fw93M5qbRv/D4ZynsO5Ht6WRKKR/izhHKFgJHS1j+qzHm7HiOi4Ga7sri1VrdA00G8Jjtcz5p8AM/rN1Oj9cX8Oa8TZw6k+fpdEopH1CqQiAilUTE5nzcUERuFBFHGeYYDnxThq9XcYjAjf/lcFR7Ou35gJXhT/Nc7Aremb+RHq8v4MsVeygo0I7qlFLuI6XpDVNEVgBdgMpY396XA6eMMXddYrs4YJYxplkJ6/QA3gU6G2OOFLPOCGAEQHR0dNKUKVMumbkoWVlZhISEuLStu2VlZRGbv5v6Wz8kPGMjRwLr8Er+3XyZmUDdMBt3NvEnvrLdY9m8eb95azbw7nyazTUVNVuPHj1WGGPaFLnQGHPJCVjp/PkH4E/Ox6tKsV0ckFrC8kRgK9CwNDmMMSQlJRlXJScnu7ytu53LVlBgzNqpxrzVzJhRYWbfu/3NbS99aOo+M8uM/2mbKSgo8Fw2L+TN2Yzx7nyazTUVNRuw3BTzuVraawQiIh2Bu4DZznl+pdy2uBesDXwF3GOM2XQlr3VVEYFmg+DhZXDdC1Q/nsLkvCf4oOrn/HNWCn+dnkqu3oCmlCpDpS0EjwHPAl8bY9JEpB6QXNIGIjIZWAQ0EpE9IjJcREaKyEjnKn8DIoF3RSRFRJa79itcpRyB0OlR+OMqpM199MyYxsIqL/PTkqX8buIyTmTnejqhUuoqUapv9caYH4EfAZwXjQ8bY/54iW3uuMTy+4H7S5nTd1WKgn5vQHxvor96gHmVRvHHbSMYNCaHCfe2pXZksKcTKqUquNK2GvpURMJEpBKwDtgoIk+5N5o6T8Pr4fcL8a/WgLGON7grYzyDRi9k+Y5iW+gqpVSplPbUUFNjTAZwEzAHqA3c465QqhiV68Dv5kKb33Ef0xnHi/xx3Fy+XrXH08mUUhVYaQuBw3nfwE3AdGNMLqCN2z3BLwD6vwU3v0cL21ZmBzzL5M+n8K9vN5Cv9xsopVxQ2kLwHrADqAQsFJE6gA6860ktbkce+IGIiEgmB7xC5Z+f55XR73Hw2HFPJ1NKVTClvVj8NvB2oVk7nTeCKU+KboqMSMY++wmGp36F7cgccv7zV05Ub0N4015QtxvEtgL7FbX0VUpd5Ur1CSEi4cAooKtz1o/AC8AJN+VSpRUYBoM+wNbvTXav/p7F339Ns/QUwve/CLwI/qEQ1wk6PAj1uns6rVLKC5X21NAEIBMY4pwygA/dFUq5IDCMWu1v4YYnP2R04//ROmcs70Y9x+kmt8D+VJh0M/z6DpSiSxGllG8pbSGob4wZZYzZ5pyeB+q5M5hyTUiAH/+9oxWPD+zIv/c1o8eGgaQMnAuN+8F3f4HpD0PeaU/HVEp5kdIWgmwR6Xz2iYh0ArTTfC8lItzTMY6pD3bEZhMGj1/Dp3Vegm7PQMon8L8BkHXQ0zGVUl6itIVgJDBaRHaIyA7gHeD3bkulykRizQhm/6ELneOj+PO0NN6VW+HWibBvDbzfHfat9nREpZQXKFUhMMasNsa0wOotNNEY0wro6dZkqkyEBzsYN7QNA1vG8q9vN/LqriaY330LCIzvDWlfezqiUsrDLmuEMmNMhvMOY4An3JBHuYHDbuOtIS25u0Ntxv64lb8ssZN//w8QkwhfDIPkVyBfR0NTylddSQNzKbMUyu1sNuHFgc0IC3Tw7oKtZGTn8uZd0/H/9kn48Z+wZCzU7wXx10ODXhBSzdORlVLl5EoKgbZDrGBEhD/1aUxYkINXv9nAydN5vHvnfwhq3A82zIYt8yDtK2vl2FbQ4DqrMJh8zwZX6mqQdQhO7IYarT2d5CIlFgIRyaToD3wBgtySSLndyG71CQt08Jdpa7n3w2V8MOw6whr3hYICOLAWNn8Hm+fBT6/Dwn9xjSMMTg6G5kOgVnuwXdYZRaVURjpM6APHd1l9hbW5z9OJzlNiITDGhJZXEFW+7mxfm9BAPx7/LIU7xy1m3NA2xIQHQUwLa+r6FJw6Clt/4NhPE4lOmQzLJ0B4bWg+yCoK0U09/Wso5f1OHoaPboJTR6BOJ5j1GOScgM6PeTjYb9z21U5EJojIQRFJLWa5iMjbIrJFRNaIiPcdL13lBrSIZdzQNmw5mEXP13/kv/M3k5Nb6DRQcBVoPpj1TZ+Ep7bAze9D1Ybwy9swpiOM6QQ//xsObbQOe3Oz9c5lpQrLPm7d1X98J9z5GdzzNSTcAt+Pgu//7jX/X9zZG9lErPsNPipm+Q1AvHNqD4xx/lTlqEfjasx7vBuvzFnPG/M2MWXZbv7ctwl9m1dHpFB7gIAQaHGbNWUdspqdrv3c+Qc96rf1xA7+Idb6/iHgX8k6cuj0GETFl/vvp5THnDkJnw6Bg+vhjikQ57wnd9AHVh9hP79lHRn0faP0p1uNscY1L2NuKwTGmIUiElfCKgOBj4wxBlgsIhEiEmOM2eeuTKpotaoEM+buJBZtPcLzM9N4+NOVtKtbhVEDmpIQG37xBiFVof0Iazq6HXYthjNZcDrT+uM/kwWns36bt/ZLWPUJNBsEXZ+Eak3K/5dUqjzl5sCUO2HPMusmzvhrf1tms0P/f0NgOPzyH+v/yE1jwO4o+rWyDlmNONZ8DolDoH3Z38srxo2HJs5CMMsY06yIZbOAV40xPzufzweeNsZcNIi9iIwARgBER0cnTZkyxaU8WVlZhISEuLStu3lLtgJj+HF3Hl9uPsPJXOhW04/esbnEVHE9m+PMcWrtnk6NvXOwFZzmUNWO7KxzGydD4q44r7fst+J4cz7N5ppLZZOCPBLS/knUkaWsb/woB6oXf+9t7Z1Tqbd9Eocj27Ku6VMU2AMAsOdlE3V4CdUO/kiVoykIBWRVimNX7Vs4GN3NpWw9evRYYYxpU+RCY4zbJiAOSC1m2Wygc6Hn84GkS71mUlKScVVycrLL27qbt2U7fvKMeX5Gmqn/7GzT6M+zzMhJy82nS3aa3UdPuv6iWYeN+f4FY16pacyoMGMm32nM3lVXlNPb9tuFvDmfZnNNidny84yZOtz6+17yfulecOk4Y0aFGzOhrzHrZ1vbv1Tdeo03E4yZN8qY/WlXnA1Ybor5XPXkiCV7gFqFntcE0j2URV0gPNjB3wY05c72tXnh819Ytes436TuB6Be1Up0ja9K14ZRdKgXSbB/Kf+MKkVCr7/CNY/Akvdg8buwYZbVkqLZIGg6ECpFufG3UqoE6SnWKZhaHaBx38vf3hiY9Tis/QJ6jYJ2D5Ruu7b3Q0A4fP172PkzBEZA4m3WaaBaHcqlubYnC8EM4BERmYJ1kfiE0esDXqdBtRB+1yyAbt26seVgFj9uOsTCzYeZvHQXE3/dgb/dRpf4KEYNSKB2ZHDpXjSoMnR/xhosZ/kESJkMs5+AOU9B/R5WUWjczzqHqq4+mQcgONI7Rs7LzYbUr2D5eNi7wjnzP1bLnr6vlf6LycH18M2fYPtC6PJ/0OUye+BJvBUiakH2MesOfz//y9v+CrntX0JEJgPdgSgR2YM1wpkDwBgzFpgD9AW2AKcA77rDQp1HRIiPDiU+OpT7u9QjJzefZTuOsnDTIaYs3U2f/yzkr/2bcnvbWue3NipJYDh0ftxqUXQgDVKnQuqXMO1BsAdA/HXQ7BYIqwlis1pLiADOn2Ij+ORu60Y4vcnN+xljHQXO+5t1r8qtEyGi9uW/xvFd1gVXv0DwC7B+2vwurzXNka3Wl5BVH0POcYhqCH3+Cc0Hw/IPrW5Xtv8IN/zL+mJS3GtnH3d20fIeBIRCvzegzfDL+53Oqt3Bte3KgDtbDd1xieUGeNhd76/cK9Bhp0t8VbrEV2VYp7o89cVqnv1qLfPWHeDVQc2pFhpY+hcTgerNrKnXKNiz3CoIaV9bp45K0A4g7e/Q4FpoeD3U72kdcSjvknPCGhRp/Uyo2xX2roL3usIt46yCXxqHN1unXnb8dPEysf1WGBzBEBBmNdEMCLO+cJx9HBACO36GbQus4tFkgPXBHdf5tw/7bk9Bk/4w7SH4crh1xND/TQit/tv7FRRYY3vMf966YSxpGPT8q3X6swLygmMzVdHViAji4+Ht+d+iHbz6zQZ6v7WQl29uTt/mMZf/YiJQq6019X4Z9q6E0yesjk5MAWCsn8b6uSFlCY3998HmubBminUfQ6321odL/PUQneCWdtfqMuxfC58PhWM74fqXoOMjcHSbNe+TwdZd7N2ftb7lFyXvDPzyb1j4GjiC4NrnrZsd805DXo5zcj7OzYHcU1bhOZ0BJw/B0a2Qk2E9zz9jHWH2eA5aD4XQ6KLfs1oTGD7POoJJfhlGt4Pe/4CWdxKasQnGv2CdSqrVHu6aCrEt3bX3yoUWAlUmbDbhvk516RIfxeOfreahT1Zyc6sa/P3GBMKDimkffckXtVsFoQT7D4TQuHt3KMi3jiQ2f2dN85+3ppBoiG5m3dRWzTlVbWR9oPiqvNOQ9jUx6WtgzUHrm7Qj6PyfwZEQ5kIhv9CqT6zrP4ERMGw21OlozY+sb33QznnK+oDfvRQGjbfuUSls5yKY+Sgc3midt+/zavEf3qWRmwN2/9KdSrT7Qac/Wterpj8C0x+CRe+QdHAdhFS37rRPHHJVfNHQQqDKVINqoXz10DW888MW3knewuJtR3i0Vzy1qgRTNTSAaqEBhAc5Sn8dobRsdqjd3pp6/RUy9lm9qe74BQ6mwZKfId85VrPYoEp961tf5TrWqQRHkPXTL7DQ80DwDy10WiHUulu6Il+PyDwAn90Ne5bSCGBTCevGtLSu0STcfPnn8nOzrQ/5VZOsU0GDxl/ctbl/MNw02jo3PudJeK8LDP4Q6nTELzcLZj4GKz60+re68wvr1N+VclzGKcuzIutbRWz5ePjpDXbVupnad//X+nu4SmghUGXOYbfx+HUN6dm4Go9/nsIzX609b7m/3UbV0IBzU/u6VRicVJOI4DJsKREWYx36tx5qPc/Ps05HHEyzWngcSIMDqdbRQ17OZbyw/Hb+OTACGvaGdiNc+5bqpu4CirVvNUy+0+r8bNB4Fu01dGzT0vrQPnuKJTcH8rKtO8bXTbMu7M77G9RoYxWFpjdBeI2iX78g33rtYztg1hNWT7aXOu0D0Poe69TK50NhYj9oez9tUz6H3BPWaaQef7a6KvEkm81qDtruAbYtWEDtq6gIgBYC5UYtakUw97Gu7Dp6ikOZpzmYedr5M4dDzsfbDmUxb90BXpu7kYEtY7mnQxzNa7qh2ajdz+owr2pD6xtuYQUF1tFCbrZ1fjk3+7fHp7OsaxRnzzEX/pmZDj+9Ab++bZ0i6PjIpbvPyEi3LoSv/QIObrAGAWp6EzTq497msmnTrNZYQVVg+FyIacHpIwtK7v+p82NWQUj72mpfP/fP1lS7I8S2hlOHIeugdR4+66D13BRY2wZVts6dl/ZCcPXmMGKBdQpm6XucCalPwLBpFf7ce0WhhUC5lcNuo37VEOpXLf6W/PX7Mpi0eCfTVu3l8+V7aFErgqEd6tAvMYZARwnfJMuKzQa2IOd1gyqXt+2RrbBoNKR8ajVFbHCddcNc3W6/fdvPPgbrZlgf/jt+Boz1Qdridmvch41zrPPW9Xs6i8INEBRRNr+bMfDjv2DBK1CzHdz28eUdvVSpa7WJ7/IEHN7yW1FYPgEqVbVO90TUhhpJ1vWYkGrW/NodL/8oKTAchnwE+1JYueEo3bQIlBstBMrjmsSE8crNzXnmhsZ8tWIPkxbv5P++WM1Ls9cxpG0tHurWgPBgFy84u1tkfatpYc/nYNl4WPo+fDTQ+oabeBvN1s6EhSuhIBciG1inSZoPtrYD62hkzzJYN92aNn0LNod1Y1389c4L3E2sVjKX68wp6yhg3TRocScM+LfVvNJVUQ2sppXdnnL9NS5FBGJbYTYtcN97qItoIVBeIyzQwbBOdbn3mjgWbT3CpMU7+eCn7SzaeoRP7m9PaKCXFgOwPqi7PQXX/MH65r/oHfjuOUL9q1i9RTa/1bqJ6sJrAjbbbxe5r38J0lda37rXzbCuX5wVEg1VGzsLQ2Oo2sT69m33t3qttDucrWGcjzP2wuQ7rKabZ5tsXgWtW5R7aCFQXkdEuKZBFNc0iGL++gP8ftIKHvhoORPva1c+p4quhCPQuvjZ6m44uo1Fa3bQvUev0m1rs0HNNtZ0/UvWh/nB9dZ0aAMcXAcr/2ddu7gksVq13Pl52bS2UVc1LQTKq/VqEs0bQ1rw2GcpPPLpSsbcnYTDXgGab4pYp39kt+vbh9e0psIXXAsK4MQu60Jz9lHrBqn8XGsqyHU+z7Mu2iYO0cGAVKloIVBeb2DLGmTm5PHctFSe/GI1bw1pic3mo6c5bDaoHGdNSpURLQSqQri7Qx0ycnL517cbCQt08MLAhLK/KU0pH6WFQFUYD3VvQEZ2HmN/3EpYkB9P9W7s6UhKXRW0EKgK5ek+jcjIyWV08lbCAh1WNwlKqSuihUBVKCLCiwObkZmTxz++2cCwBH+6ezqUUhWcW5tfiEgfEdkoIltE5JkiloeLyEwRWS0iaSKig9OoS7LbhDeHtKBn42pMTDvDwNG/MDp5C5sOZJ4d/1opdRncVghExA6MBm4AmgJ3iEjTC1Z7GFhnjGmBNZrZGyJSvmO0qQrJYbfx7l2tGdzQAcbw2tyNXP/WQrq/voAXZ61j8bYj5OUXeDqmUhWCO08NtQO2GGO2ATjHJh4IrCu0jgFCxWr+EQIcBfLcmEldRQIddvrX86d7984cyMjh+/UHmLfuAJMW7WT8z9uJCHZwXZNo7ulYh8SaEZ6Oq5TXEncdSovIYKCPMeZ+5/N7gPbGmEcKrROKNYh9YyAUuM0YM7uI1xoBjACIjo5OmjJlikuZsrKyCAkpvvMzT9JsrikqW3aeIfVwPqsO5rPyQB45+dAgwsZ1dRwkRdvxK8d7ECravvMWms01JWXr0aPHCmNMmyIXGmPcMgG3Ah8Uen4P8N8L1hkMvAUI0ADYDoSV9LpJSUnGVcnJyS5v626azTWXypaRfcaM/2mb6fqvH0ydp2eZ9i9/b975YbM5knXaK/J5kmZzTUXNBiw3xXyuuvPU0B6gVqHnNYH0C9a5D3jVGXKLiGzHOjpY6sZcyoeEBjr4Xee6DLsmjuSNB5n46w5em7uR/8zfzE0tYxnYsga1qwQTHRaIv18F6LpCKTdwZyFYBsSLSF1gL3A7cOcF6+wCegE/iUg00AjY5sZMykfZbEKvJtH0ahLN5gOZTPx1B1+ttMY/AKtrn6ohAcRGBFEjIoiY8EBiI4Lo1aQadSI9PDqWUm7mtkJgjMkTkUeAuYAdmGCMSRORkc7lY4EXgYkishbr9NDTxpjD7sqkFEB8dCgv39ycP/VpzNo9J0g/kU36cWvadyKH9fszmL/hADm5Bbz+3Ub+NTiR/omxno6tlNu49YYyY8wcYM4F88YWepwOaB+5yiPCgxx0jo8qcpkxht1Hs3n88xQe+XQVK3Ye4899m1SMnk+Vukz6V61UEUSE2pHBTH6gA/d1iuPDX3Zwx/uLOZBxOQPdK1UxaCFQqgT+fjZGDUjg7TtasW5fBv3e/olFW494OpZSZUoLgVKlcGOLWKY/3InwIAd3fbCYsT9u1e4s1FVDC4FSpRQfHcr0RzpzQ7MYXv1mA7+ftIKl24+y70Q2BQVaFFTFpb2PKnUZQgL8eOfOVrT+pTL/mLOe79YdAMDfbqNG5SBqVg6idpVgalUJJi6yEpKnBUJ5Py0ESl0mEWF457r0ax7DxgOZ7D56it3HTrHnaDa7jp5i7d59HD+VC0CgHQZlruWOdrVpViPcw8mVKpoWAqVcVD08kOrhgUUuy8zJJS09g//OWs7UFXv4ZMkumtcI5/Z2tbixRSyhgY5yTqtU8fQagVJuEBrooEO9SB5IDGDpn6/l+RsTyM0v4C9fp9L+lfk8PXUNa/Yc93RMpQA9IlDK7cKDHdx7TRxDO9Zh9Z4TTFm6ixmr0/ls+W76Jcbw7A2NqVk52NMxlQ/TQqBUORERWtaKoGWtCJ7r35TxP21nzI9b+H7dAX7frT4ju9Uj2F//S6ryp6eGlPKAkAA/Hr02nh/+rzu9E6rz9vzN9HrjR6an7NX7E1S500KglAfFRgTx9h2t+GJkR6pU8ufRKSncOnYRa/ecOG+9ggLDsZNn2HIwi2U7jjI3bT+7j57yUGp1tdHjUKW8QNu4Ksx4pDNfLN/Na3M3cuPon2lRM4Ks03kcO3mGY6fOcOE9awF+Np68vhG/61wXezmOuqauPloIlPISdptwe7va9E2MYXTyFlJ2HScmPITKlfyJrORP5WB/IkOsn5UC/BizYCsvz1nP7LX7eG1wIvHRoZ7+FVQFpYVAKS8TFujg2RuaXHK9cUOTmLE6nb/PSKPf2z/z6LXxjOhaT7vKVpfNrX8xItJHRDaKyBYReaaYdbqLSIqIpInIj+7Mo9TVREQY2LIG857oxnUJ0bw2dyM3jf6FtPQTl95YqULcVghExA6MBm4AmgJ3iEjTC9aJAN4FbjTGJGANeK+UugxRIQGMvrM1Y+9uzYGM0wx85xfe/G4jR0+e8XQ0VUG489RQO2CLMWYbgIhMAQYC6wqtcyfwlTFmF4Ax5qAb8yh1VevTLIb2dSN5cdY63v5hC2//sIWY8EASYsNIiA23ftYIJzY8EBG9uKx+I+5qsywig4E+xpj7nc/vAdobYx4ptM6/AQeQAIQC/zHGfFTEa40ARgBER0cnTZkyxaVMWVlZhISEuLStu2k213hzNvBcvq3H89l0rICdGfnsyihg30nD2f/pIQ6IC7fTo3oeSTW9c995879rRc3Wo0ePFcaYNkUtc+cRQVFfOS6sOn5AEtALCAIWichiY8ym8zYy5n3gfYA2bdqY7t27uxRowYIFuLqtu2k213hzNvBcvgvf8dSZPNbvy2Rd+gnS0jP4afNh/puaT28Twl/6NqV2pHd1ceHN/65XYzZ3FoI9QK1Cz2sC6UWsc9gYcxI4KSILgRbAJpRSZSbY34+kOpVJqlMZgJzcfP7y0Xy+2XyYa9/6kQe61OWh7g2oFKANCX2RO1sNLQPiRaSuiPgDtwMzLlhnOtBFRPxEJBhoD6x3YyalFBDosDOgvj8//F93+jWPYXTyVnq+sYBpq7SLC1/ktvJvjMkTkUeAuYAdmGCMSRORkc7lY40x60XkW2ANUAB8YIxJdVcmpdT5qocH8tZtLbm7Q22en7mOxz5L4aNFO3j8uoYE+/txOjefnLx8cnILyMn97WeVSv70bFKNMB1X4arg1uNAY8wcYM4F88Ze8Pw14DV35lBKlSypThWmPdSJqSv28K+5G7hn/NJLbuNvt9G1YVUGtIjh2ibRelqpAtN/OaUUADabMKRtLW5oXp0l245itwuBfnYCHTYCHXbnZCPQz872IyeZtXofs9em8/36AwQ6bPRsXI3+ibH0aFSNIH+7p38ddRm0ECilzhMa6ODaptElrlO5kj+ta1fmuX5NWL7zGLPWpDNn7T7mrN1PsL+da5tEM6BFLF0bRhHgp0XB22khUEq5zGYT2tWtQru6VRg1IIEl244wc80+vk3dx4zV6YQG+tE7oTr9E2Po1CBK+0HyUloIlFJlwm4TrmkQxTUNonhhYAK/bDnMzNX7mJu6n6kr9lA52EGfZjEMaBFDh7qR2LTrbK+hhUApVeYcdhvdG1Wje6Nq5OQ2Y+GmQ8xas4/pKXuZvHQXDaNDeLRXQ25oVl0LghfQQqCUcqtAh53rE6pzfUJ1ss/k823aPt75YQsPf7qSxtVDebRXPL0TtCB4kp6wU0qVmyB/Oze3qsl3j3fjP7e35Ex+AQ9+spK+b//Et6n79WY2D9EjAqVUubPbrLEU+ifGMnN1Om/P38zIj1fQNCaMR6+Nx37huJzKrbQQKKU8xm4TbmpVg/6JMcxwFoTfT1qBvw3abVtC+7pV6FA/ksSa4doM1Y20ECilPM7PbuOW1jW5sUUsyRsP8fmPKew5eYY35m2CeRDosNG6dmU61IukU4NIWteurGMqlCEtBEopr+Fnt3Fd02gcBwPo3r0Lx06eYemOoyzedoQl247y1vebeHMe1IuqxO3tajE4qRZVKvl7OnaFp4VAKeW1Klfyp3dCdXonVAfg+KkzfL/+IFOW7uKVORt4fe4mejerzh3tatGxXqQeJbhIC4FSqsKICPZncFJNBifVZNOBTCYv3cWXK/Ywc3U6daMqcXvbWtzSuiZVQwM8HbVC0UKglKqQGkaHMmpAAk/3acw3qfv4dMku/vHNBv7xzQYaRofQoV4k7etG0r5eFaJCtDCURAuBUqpCC3RY9ybc3Kommw9kMm/9ARZvO8rUFXv4aNFOABpUC7FaINWLpGt8VcKDdRyFwtxaCESkD/AfrIFpPjDGvFrMem2BxcBtxpip7syklLp6xUeHEh8dykPdITe/gNS9J1iy3brYPG3VXj5Zsotgfzt3tqvN/V3qUT080NORvYLbCoGI2IHRwHVYYxMvE5EZxph1Raz3T6yRzJRSqkw47DZa1a5Mq9qVGdmtPnn5BazZe4JJi3by4a87+GjRTgYl1eD3XesTF1XJ03E9yp1HBO2ALcaYbQAiMgUYCKy7YL0/AF8Cbd2YRSnl4/zs1r0IrWtX5onrGvLewq18vnwPny3bTb/EWB7sVp+msWGejukR7iwENYDdhZ7vwRqc/hwRqQHcDPREC4FSqpzUqhLMSzc154+94hn/83Y+XrSTmavT6dGoKo1jws6Nz3w6L5/TZ8drzssHoFVIHt2Muaqaqoq7OnkSkVuB3saY+53P7wHaGWP+UGidL4A3jDGLRWQiMKuoawQiMgIYARAdHZ00ZcoUlzJlZWUREhLi0rbuptlc483ZwLvzabbfnMw1zN+Vy/c7czmZC/52cNgEfzv428BhFxw2yDxjOHDK0KKqnXua+hMV5F39dpa033r06LHCGNOmyIXGGLdMQEdgbqHnzwLPXrDOdmCHc8oCDgI3lfS6SUlJxlXJyckub+tums013pzNGO/Op9kuX25evnl6wnem8XPfmMbPfWPGLthizuTlezrWOSXtN2C5KeZz1Z3lbBkQLyJ1RcQfuB2YcUERqmuMiTPGxAFTgYeMMdPcmEkppVzmZ7fRp66D7/+vG50aRPKPbzYw4L8/s3LXMU9HuyJuKwTGmDzgEazWQOuBz40xaSIyUkRGuut9lVLK3WpEBDFuaBvG3p3E8VO5DBrzK3/5ei0nsnM9Hc0lbr2PwBgzB5hzwbyxxaw7zJ1ZlFKqLIkIfZpVp3N8FG98t5H//bqDb1L3c2OLWG5sGUurWhEV5oKy3lmslFJXICTAj1EDErilVU1GJ2/h06W7mPjrDmpVCWJAolUUGlcvulmqMYYDGafZdCCTzQezqBzs4Nqm0YQFlu+dz1oIlFKqDDSvGc7Ye5LIyMnlu7QDzFidznsLt/Hugq00jA5hQGIszWqGs+3QSTYfyDz34Z+Zk3fe6/jbbXRrVJUBLWK5tkk1gv3d/zGthUAppcpQWKDjXA+ph7NO883afcxYnW4NsuNUpZI/8dVCGNgylobRocRXC6VBtRD2HDvFzNX7mL02nXnrDhDksNOzSTUGJMbSvVFVAh3uGaVNC4FSSrlJVEgA93SM456OcaQfz2b30VPUrxZSbG+oVUMDaFW7Ms/1a8KyHUeZtWYfc9buY/aafYQE+PHYtfHc36VemefUQqCUUuUgNiKI2IigUq1rswnt60XSvl4kowY0ZfG2o8xcne62TvK0ECillBfzs9voHB9F5/got72Hd90frZRSqtxpIVBKKR+nhUAppXycFgKllPJxWgiUUsrHaSFQSikfp4VAKaV8nBYCpZTycW4bqtJdROQQsNPFzaOAw2UYpyxpNtd4czbw7nyazTUVNVsdY0zVohZUuEJwJURkuSluzE4P02yu8eZs4N35NJtrrsZsempIKaV8nBYCpZTycb5WCN73dIASaDbXeHM28O58ms01V102n7pGoJRS6mK+dkSglFLqAloIlFLKx/lMIRCRPiKyUUS2iMgzns5TmIjsEJG1IpIiIss9nGWCiBwUkdRC86qIyDwR2ez8WdmLsv1dRPY6912KiPT1ULZaIpIsIutFJE1EHnXO9/i+KyGbx/ediASKyFIRWe3M9rxzvjfst+KyeXy/FcpoF5FVIjLL+dyl/eYT1whExA5sAq4D9gDLgDuMMes8GsxJRHYAbYwxHr9JRUS6AlnAR8aYZs55/wKOGmNedRbRysaYp70k29+BLGPM6+Wd54JsMUCMMWaliIQCK4CbgGF4eN+VkG0IHt53IiJAJWNMlog4gJ+BR4Fb8Px+Ky5bH7zgbw5ARJ4A2gBhxpj+rv5f9ZUjgnbAFmPMNmPMGWAKMNDDmbySMWYhcPSC2QOB/zkf/w/rQ6TcFZPNKxhj9hljVjofZwLrgRp4wb4rIZvHGUuW86nDORm8Y78Vl80riEhNoB/wQaHZLu03XykENYDdhZ7vwUv+IzgZ4DsRWSEiIzwdpgjRxph9YH2oANU8nOdCj4jIGuepI4+ctipMROKAVsASvGzfXZANvGDfOU9vpAAHgXnGGK/Zb8VkAy/Yb8C/gT8BBYXmubTffKUQSBHzvKayA52MMa2BG4CHnadAVOmMAeoDLYF9wBueDCMiIcCXwGPGmAxPZrlQEdm8Yt8ZY/KNMS2BmkA7EWnmiRxFKSabx/ebiPQHDhpjVpTF6/lKIdgD1Cr0vCaQ7qEsFzHGpDt/HgS+xjqV5U0OOM8znz3ffNDDec4xxhxw/mctAMbhwX3nPI/8JfCJMeYr52yv2HdFZfOmfefMcxxYgHUO3iv221mFs3nJfusE3Oi8vjgF6CkiH+PifvOVQrAMiBeRuiLiD9wOzPBwJgBEpJLzAh4iUgm4HkgteatyNwO41/n4XmC6B7Oc5+wfvdPNeGjfOS8sjgfWG2PeLLTI4/uuuGzesO9EpKqIRDgfBwHXAhvwjv1WZDZv2G/GmGeNMTWNMXFYn2c/GGPuxtX9ZozxiQnoi9VyaCvwF0/nKZSrHrDaOaV5OhswGetwNxfrSGo4EAnMBzY7f1bxomyTgLXAGud/ghgPZeuMdbpxDZDinPp6w74rIZvH9x2QCKxyZkgF/uac7w37rbhsHt9vF+TsDsy6kv3mE81HlVJKFc9XTg0ppZQqhhYCpZTycVoIlFLKx2khUEopH6eFQCmlfJwWAqUuICL5hXqWTJEy7K1WROKkUO+pSnkDP08HUMoLZRurWwGlfIIeEShVSmKNG/FPZx/1S0WkgXN+HRGZ7+yEbL6I1HbOjxaRr5392a8WkWucL2UXkXHOPu6/c961qpTHaCFQ6mJBF5wauq3QsgxjTDvgHazeH3E+/sgYkwh8ArztnP828KMxpgXQGuvOcYB4YLQxJgE4Dgxy62+j1CXoncVKXUBEsowxIUXM3wH0NMZsc3bitt8YEykih7G6Gch1zt9njIkSkUNATWPM6UKvEYfVnXG88/nTgMMY81I5/GpKFUmPCJS6PKaYx8WtU5TThR7no9fqlIdpIVDq8txW6Oci5+NfsXqABLgLa0hDsDr9ehDODXASVl4hlboc+k1EqYsFOUelOutbY8zZJqQBIrIE60vUHc55fwQmiMhTwCHgPuf8R4H3RWQ41jf/B7F6T1XKq+g1AqVKyXmNoI0x5rCnsyhVlvTUkFJK+Tg9IlBKKR+nRwRKKeXjtBAopZSP00KglFI+TguBUkr5OC0ESinl4/4fpUiNdz8nHtYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n\\nmetrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\\naggreg_metrics = []\\nagg_col = \\\"epoch\\\"\\nfor i, dfg in metrics.groupby(agg_col):\\n    agg = dict(dfg.mean())\\n    agg[agg_col] = i\\n    aggreg_metrics.append(agg)\\n\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\ndf_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n)\\ndf_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n\\nmetrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\\naggreg_metrics = []\\nagg_col = \\\"epoch\\\"\\nfor i, dfg in metrics.groupby(agg_col):\\n    agg = dict(dfg.mean())\\n    agg[agg_col] = i\\n    aggreg_metrics.append(agg)\\n\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\ndf_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n)\\ndf_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n)\\n\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/logger_csv_plot_basic.py\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"valid_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "df_metrics[[\"train_acc\", \"valid_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00611a4e",
   "metadata": {},
   "source": [
    "- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "86a2c9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_19/checkpoints/epoch=36-step=6474.ckpt\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "Loaded model weights from checkpoint at logs/my-model/version_19/checkpoints/epoch=36-step=6474.ckpt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4490fa909a154f87b540d061b971a5ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_acc': 0.7178000211715698}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.7178000211715698}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\";\n",
       "                var nbb_formatted_code = \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\"best\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b49bb38",
   "metadata": {},
   "source": [
    "## Predicting labels of new data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffac9f60",
   "metadata": {},
   "source": [
    "- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\n",
    "- Alternatively, we can also manually load the best model from a checkpoint as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20818ae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logs/my-model/version_19/checkpoints/epoch=36-step=6474.ckpt\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"path = trainer.checkpoint_callback.best_model_path\\nprint(path)\";\n",
       "                var nbb_formatted_code = \"path = trainer.checkpoint_callback.best_model_path\\nprint(path)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = trainer.checkpoint_callback.best_model_path\n",
    "print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "094f161f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\nlightning_model.eval();\";\n",
       "                var nbb_formatted_code = \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\nlightning_model.eval()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\n",
    "lightning_model.eval();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "475eacb7",
   "metadata": {},
   "source": [
    "- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\n",
    "- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "610b9515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([7, 3, 9, 0, 8])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/datamodule_testloader.py\\ntest_dataloader = data_module.test_dataloader()\\nacc = torchmetrics.Accuracy()\\n\\nfor batch in test_dataloader:\\n    features, true_labels = batch\\n\\n    with torch.no_grad():\\n        logits = lightning_model(features)\\n\\n    predicted_labels = torch.argmax(logits, dim=1)\\n    acc(predicted_labels, true_labels)\\n\\npredicted_labels[:5]\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/datamodule_testloader.py\\ntest_dataloader = data_module.test_dataloader()\\nacc = torchmetrics.Accuracy()\\n\\nfor batch in test_dataloader:\\n    features, true_labels = batch\\n\\n    with torch.no_grad():\\n        logits = lightning_model(features)\\n\\n    predicted_labels = torch.argmax(logits, dim=1)\\n    acc(predicted_labels, true_labels)\\n\\npredicted_labels[:5]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/datamodule_testloader.py\n",
    "test_dataloader = data_module.test_dataloader()\n",
    "acc = torchmetrics.Accuracy()\n",
    "\n",
    "for batch in test_dataloader:\n",
    "    features, true_labels = batch\n",
    "\n",
    "    with torch.no_grad():\n",
    "        logits = lightning_model(features)\n",
    "\n",
    "    predicted_labels = torch.argmax(logits, dim=1)\n",
    "    acc(predicted_labels, true_labels)\n",
    "\n",
    "predicted_labels[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda9dad7",
   "metadata": {},
   "source": [
    "- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ab1373cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.7178 (71.78%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"test_acc = acc.compute()\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\";\n",
       "                var nbb_formatted_code = \"test_acc = acc.compute()\\nprint(f\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_acc = acc.compute()\n",
    "print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f783531",
   "metadata": {},
   "source": [
    "## Inspecting Failure Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd991baa",
   "metadata": {},
   "source": [
    "- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\n",
    "- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6fea8a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"class_dict = {0: 'airplane',\\n              1: 'automobile',\\n              2: 'bird',\\n              3: 'cat',\\n              4: 'deer',\\n              5: 'dog',\\n              6: 'frog',\\n              7: 'horse',\\n              8: 'ship',\\n              9: 'truck'}\";\n",
       "                var nbb_formatted_code = \"class_dict = {\\n    0: \\\"airplane\\\",\\n    1: \\\"automobile\\\",\\n    2: \\\"bird\\\",\\n    3: \\\"cat\\\",\\n    4: \\\"deer\\\",\\n    5: \\\"dog\\\",\\n    6: \\\"frog\\\",\\n    7: \\\"horse\\\",\\n    8: \\\"ship\\\",\\n    9: \\\"truck\\\",\\n}\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_dict = {0: 'airplane',\n",
    "              1: 'automobile',\n",
    "              2: 'bird',\n",
    "              3: 'cat',\n",
    "              4: 'deer',\n",
    "              5: 'dog',\n",
    "              6: 'frog',\n",
    "              7: 'horse',\n",
    "              8: 'ship',\n",
    "              9: 'truck'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c96bfe90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n# Append the folder that contains the\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n# files so we can import from them\\n\\nimport sys\\n\\nsys.path.append(\\\"../../pytorch_ipynb\\\")\\n\\nfrom helper_plotting import show_examples\\n\\n\\nshow_examples(\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n)\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n# Append the folder that contains the\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n# files so we can import from them\\n\\nimport sys\\n\\nsys.path.append(\\\"../../pytorch_ipynb\\\")\\n\\nfrom helper_plotting import show_examples\\n\\n\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/plot_failurecases_basic.py\n",
    "# Append the folder that contains the\n",
    "# helper_data.py, helper_plotting.py, and helper_evaluate.py\n",
    "# files so we can import from them\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"../../pytorch_ipynb\")\n",
    "\n",
    "from helper_plotting import show_examples\n",
    "\n",
    "\n",
    "show_examples(\n",
    "    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0aadb9",
   "metadata": {},
   "source": [
    "- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "efd81a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\nfrom torchmetrics import ConfusionMatrix\\nimport matplotlib\\nfrom mlxtend.plotting import plot_confusion_matrix\\n\\n\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\n\\nfor x, y in test_dataloader:\\n\\n    with torch.no_grad():\\n        pred = lightning_model(x)\\n    cmat(pred, y)\\n\\ncmat_tensor = cmat.compute()\\ncmat = cmat_tensor.numpy()\\n\\nfig, ax = plot_confusion_matrix(\\n    conf_mat=cmat,\\n    class_names=class_dict.values(),\\n    norm_colormap=matplotlib.colors.LogNorm()  \\n    # normed colormaps highlight the off-diagonals \\n    # for high-accuracy models better\\n)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\nfrom torchmetrics import ConfusionMatrix\\nimport matplotlib\\nfrom mlxtend.plotting import plot_confusion_matrix\\n\\n\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\n\\nfor x, y in test_dataloader:\\n\\n    with torch.no_grad():\\n        pred = lightning_model(x)\\n    cmat(pred, y)\\n\\ncmat_tensor = cmat.compute()\\ncmat = cmat_tensor.numpy()\\n\\nfig, ax = plot_confusion_matrix(\\n    conf_mat=cmat,\\n    class_names=class_dict.values(),\\n    norm_colormap=matplotlib.colors.LogNorm()\\n    # normed colormaps highlight the off-diagonals\\n    # for high-accuracy models better\\n)\\n\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\n",
    "from torchmetrics import ConfusionMatrix\n",
    "import matplotlib\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "\n",
    "cmat = ConfusionMatrix(num_classes=len(class_dict))\n",
    "\n",
    "for x, y in test_dataloader:\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pred = lightning_model(x)\n",
    "    cmat(pred, y)\n",
    "\n",
    "cmat_tensor = cmat.compute()\n",
    "cmat = cmat_tensor.numpy()\n",
    "\n",
    "fig, ax = plot_confusion_matrix(\n",
    "    conf_mat=cmat,\n",
    "    class_names=class_dict.values(),\n",
    "    norm_colormap=matplotlib.colors.LogNorm()  \n",
    "    # normed colormaps highlight the off-diagonals \n",
    "    # for high-accuracy models better\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "242fb162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torchmetrics     : 0.6.2\n",
      "pytorch_lightning: 1.5.1\n",
      "pandas           : 1.4.1\n",
      "sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \n",
      "[GCC 9.4.0]\n",
      "torchvision      : 0.11.2\n",
      "numpy            : 1.22.0\n",
      "torch            : 1.10.1\n",
      "matplotlib       : 3.3.4\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"%watermark --iversions\";\n",
       "                var nbb_formatted_code = \"%watermark --iversions\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%watermark --iversions"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
